Volunteer Summary

CONSORT Flow Diagram

Overall status

Characteristic

Overall1

Control1

Treatment1

time_point

1st

118

58

60

2nd

82

48

34

1n

Demographic information

Characteristic

N

Overall, N = 1181

control, N = 581

treatment, N = 601

p-value2

age

118

38.41 ± 17.09 (18 - 148)

39.90 ± 19.46 (18 - 148)

36.97 ± 14.46 (20 - 70)

0.354

gender

118

0.175

female

86 (73%)

39 (67%)

47 (78%)

male

32 (27%)

19 (33%)

13 (22%)

occupation

118

0.633

civil

6 (5.1%)

2 (3.4%)

4 (6.7%)

clerk

23 (19%)

9 (16%)

14 (23%)

homemaker

8 (6.8%)

3 (5.2%)

5 (8.3%)

manager

16 (14%)

9 (16%)

7 (12%)

other

11 (9.3%)

4 (6.9%)

7 (12%)

professional

15 (13%)

11 (19%)

4 (6.7%)

retired

4 (3.4%)

2 (3.4%)

2 (3.3%)

service

5 (4.2%)

2 (3.4%)

3 (5.0%)

student

28 (24%)

15 (26%)

13 (22%)

unemploy

2 (1.7%)

1 (1.7%)

1 (1.7%)

working_status

118

76 (64%)

37 (64%)

39 (65%)

0.891

marital

118

0.461

divorced

4 (3.4%)

1 (1.7%)

3 (5.0%)

married

27 (23%)

15 (26%)

12 (20%)

single

86 (73%)

41 (71%)

45 (75%)

widowed

1 (0.8%)

1 (1.7%)

0 (0%)

marital_r

118

0.690

married

27 (23%)

15 (26%)

12 (20%)

other

5 (4.2%)

2 (3.4%)

3 (5.0%)

single

86 (73%)

41 (71%)

45 (75%)

education

118

0.038

primary

0 (0%)

0 (0%)

0 (0%)

secondary

14 (12%)

3 (5.2%)

11 (18%)

post-secondary

18 (15%)

12 (21%)

6 (10%)

university

86 (73%)

43 (74%)

43 (72%)

university_edu

118

86 (73%)

43 (74%)

43 (72%)

0.763

family_income

118

0.664

0_10000

12 (10%)

5 (8.6%)

7 (12%)

10001_20000

21 (18%)

8 (14%)

13 (22%)

20001_30000

23 (19%)

11 (19%)

12 (20%)

30001_40000

20 (17%)

10 (17%)

10 (17%)

40000_above

42 (36%)

24 (41%)

18 (30%)

high_income

118

62 (53%)

34 (59%)

28 (47%)

0.194

religion

118

0.674

buddhism

5 (4.2%)

4 (6.9%)

1 (1.7%)

catholic

5 (4.2%)

2 (3.4%)

3 (5.0%)

christianity

46 (39%)

23 (40%)

23 (38%)

nil

60 (51%)

29 (50%)

31 (52%)

other

1 (0.8%)

0 (0%)

1 (1.7%)

taoism

1 (0.8%)

0 (0%)

1 (1.7%)

religion_r

118

0.957

christianity

51 (43%)

25 (43%)

26 (43%)

nil

60 (51%)

29 (50%)

31 (52%)

other

7 (5.9%)

4 (6.9%)

3 (5.0%)

source

118

0.084

bokss

50 (42%)

20 (34%)

30 (50%)

facebook

17 (14%)

13 (22%)

4 (6.7%)

instagram

9 (7.6%)

6 (10%)

3 (5.0%)

other

19 (16%)

9 (16%)

10 (17%)

refresh

23 (19%)

10 (17%)

13 (22%)

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Characteristic

N

Overall, N = 1181

control, N = 581

treatment, N = 601

p-value2

sets

118

19.19 ± 2.19 (15 - 25)

19.02 ± 2.03 (15 - 24)

19.37 ± 2.34 (15 - 25)

0.388

setv

118

11.17 ± 1.60 (8 - 15)

11.03 ± 1.56 (8 - 15)

11.30 ± 1.65 (8 - 15)

0.371

maks

118

44.91 ± 3.62 (36 - 57)

44.67 ± 3.59 (36 - 52)

45.13 ± 3.67 (38 - 57)

0.492

ibs

118

15.40 ± 2.43 (5 - 20)

15.41 ± 2.14 (10 - 20)

15.38 ± 2.70 (5 - 20)

0.946

ers_e

118

12.21 ± 1.47 (8 - 15)

12.14 ± 1.47 (8 - 15)

12.28 ± 1.47 (9 - 15)

0.592

ers_r

118

11.12 ± 1.58 (7 - 15)

11.02 ± 1.57 (7 - 14)

11.22 ± 1.58 (8 - 15)

0.494

pss_pa

118

44.72 ± 4.43 (30 - 54)

44.47 ± 4.26 (30 - 54)

44.97 ± 4.61 (31 - 54)

0.541

pss_ps

118

26.47 ± 8.18 (12 - 56)

26.67 ± 7.63 (13 - 42)

26.27 ± 8.73 (12 - 56)

0.789

pss

118

44.75 ± 11.64 (21 - 77)

45.21 ± 11.26 (22 - 72)

44.30 ± 12.07 (21 - 77)

0.674

rki_responsible

118

21.03 ± 4.14 (7 - 32)

20.95 ± 4.11 (13 - 29)

21.12 ± 4.19 (7 - 32)

0.826

rki_nonlinear

118

13.26 ± 2.69 (6 - 22)

13.12 ± 2.54 (6 - 20)

13.40 ± 2.85 (7 - 22)

0.576

rki_peer

118

20.55 ± 2.15 (16 - 25)

20.47 ± 2.07 (16 - 25)

20.63 ± 2.25 (16 - 25)

0.674

rki_expect

118

4.75 ± 1.09 (2 - 8)

4.60 ± 1.11 (2 - 8)

4.90 ± 1.05 (2 - 7)

0.139

rki

118

59.60 ± 6.05 (44 - 81)

59.14 ± 5.86 (45 - 76)

60.05 ± 6.23 (44 - 81)

0.415

raq_possible

118

15.64 ± 1.80 (12 - 20)

15.74 ± 1.89 (12 - 20)

15.53 ± 1.72 (12 - 20)

0.533

raq_difficulty

118

12.42 ± 1.40 (9 - 15)

12.53 ± 1.38 (9 - 15)

12.30 ± 1.43 (9 - 15)

0.367

raq

118

28.05 ± 2.92 (21 - 35)

28.28 ± 2.97 (21 - 35)

27.83 ± 2.88 (21 - 35)

0.413

who

118

14.71 ± 4.36 (6 - 25)

14.62 ± 4.24 (6 - 25)

14.80 ± 4.50 (6 - 25)

0.824

phq

118

3.70 ± 3.77 (0 - 18)

3.66 ± 3.73 (0 - 17)

3.75 ± 3.83 (0 - 18)

0.892

gad

118

3.21 ± 3.56 (0 - 21)

3.38 ± 4.11 (0 - 21)

3.05 ± 2.95 (0 - 12)

0.617

nb_pcs

118

51.63 ± 7.19 (25 - 63)

51.88 ± 7.17 (25 - 63)

51.38 ± 7.27 (27 - 62)

0.709

nb_mcs

118

50.39 ± 8.55 (22 - 70)

50.20 ± 8.89 (22 - 68)

50.57 ± 8.27 (35 - 70)

0.816

1Mean ± SD (Range)

2Two Sample t-test

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

sets

(Intercept)

19.0

0.276

18.5, 19.6

group

control

—

—

—

treatment

0.349

0.387

-0.410, 1.11

0.368

time_point

1st

—

—

—

2nd

-0.201

0.319

-0.827, 0.425

0.531

group * time_point

treatment * 2nd

0.374

0.484

-0.575, 1.32

0.442

Pseudo R square

0.016

setv

(Intercept)

11.0

0.214

10.6, 11.5

group

control

—

—

—

treatment

0.266

0.300

-0.322, 0.853

0.377

time_point

1st

—

—

—

2nd

0.299

0.224

-0.140, 0.739

0.185

group * time_point

treatment * 2nd

-0.156

0.341

-0.826, 0.513

0.648

Pseudo R square

0.008

maks

(Intercept)

44.7

0.485

43.7, 45.6

group

control

—

—

—

treatment

0.461

0.680

-0.872, 1.79

0.499

time_point

1st

—

—

—

2nd

-0.254

0.418

-1.07, 0.566

0.545

group * time_point

treatment * 2nd

0.330

0.642

-0.928, 1.59

0.609

Pseudo R square

0.007

ibs

(Intercept)

15.4

0.309

14.8, 16.0

group

control

—

—

—

treatment

-0.030

0.434

-0.880, 0.820

0.944

time_point

1st

—

—

—

2nd

0.128

0.257

-0.376, 0.633

0.620

group * time_point

treatment * 2nd

0.503

0.395

-0.272, 1.28

0.207

Pseudo R square

0.009

ers_e

(Intercept)

12.1

0.189

11.8, 12.5

group

control

—

—

—

treatment

0.145

0.264

-0.373, 0.664

0.583

time_point

1st

—

—

—

2nd

-0.302

0.184

-0.662, 0.059

0.104

group * time_point

treatment * 2nd

0.429

0.281

-0.121, 0.979

0.130

Pseudo R square

0.019

ers_r

(Intercept)

11.0

0.192

10.6, 11.4

group

control

—

—

—

treatment

0.199

0.269

-0.328, 0.727

0.460

time_point

1st

—

—

—

2nd

0.096

0.232

-0.359, 0.551

0.680

group * time_point

treatment * 2nd

0.176

0.351

-0.512, 0.864

0.618

Pseudo R square

0.012

pss_pa

(Intercept)

44.5

0.581

43.3, 45.6

group

control

—

—

—

treatment

0.501

0.815

-1.10, 2.10

0.539

time_point

1st

—

—

—

2nd

-0.950

0.633

-2.19, 0.291

0.137

group * time_point

treatment * 2nd

0.358

0.963

-1.53, 2.24

0.711

Pseudo R square

0.014

pss_ps

(Intercept)

26.7

1.044

24.6, 28.7

group

control

—

—

—

treatment

-0.406

1.464

-3.27, 2.46

0.782

time_point

1st

—

—

—

2nd

0.936

0.958

-0.941, 2.81

0.331

group * time_point

treatment * 2nd

-1.55

1.466

-4.43, 1.32

0.292

Pseudo R square

0.007

pss

(Intercept)

45.2

1.487

42.3, 48.1

group

control

—

—

—

treatment

-0.907

2.086

-5.00, 3.18

0.664

time_point

1st

—

—

—

2nd

1.90

1.356

-0.754, 4.56

0.164

group * time_point

treatment * 2nd

-1.82

2.076

-5.89, 2.25

0.383

Pseudo R square

0.010

rki_responsible

(Intercept)

20.9

0.538

19.9, 22.0

group

control

—

—

—

treatment

0.168

0.754

-1.31, 1.65

0.824

time_point

1st

—

—

—

2nd

0.134

0.521

-0.887, 1.15

0.798

group * time_point

treatment * 2nd

-0.115

0.796

-1.67, 1.44

0.886

Pseudo R square

0.000

rki_nonlinear

(Intercept)

13.1

0.371

12.4, 13.8

group

control

—

—

—

treatment

0.279

0.520

-0.740, 1.30

0.592

time_point

1st

—

—

—

2nd

-0.236

0.369

-0.959, 0.486

0.523

group * time_point

treatment * 2nd

0.540

0.563

-0.563, 1.64

0.340

Pseudo R square

0.010

rki_peer

(Intercept)

20.5

0.288

19.9, 21.0

group

control

—

—

—

treatment

0.168

0.403

-0.623, 0.958

0.678

time_point

1st

—

—

—

2nd

0.083

0.290

-0.486, 0.651

0.776

group * time_point

treatment * 2nd

0.085

0.443

-0.782, 0.952

0.848

Pseudo R square

0.003

rki_expect

(Intercept)

4.60

0.135

4.34, 4.87

group

control

—

—

—

treatment

0.297

0.189

-0.073, 0.667

0.118

time_point

1st

—

—

—

2nd

0.121

0.159

-0.191, 0.433

0.450

group * time_point

treatment * 2nd

0.097

0.241

-0.376, 0.570

0.688

Pseudo R square

0.030

rki

(Intercept)

59.1

0.791

57.6, 60.7

group

control

—

—

—

treatment

0.912

1.110

-1.26, 3.09

0.412

time_point

1st

—

—

—

2nd

0.112

0.768

-1.39, 1.62

0.884

group * time_point

treatment * 2nd

0.621

1.174

-1.68, 2.92

0.598

Pseudo R square

0.010

raq_possible

(Intercept)

15.7

0.232

15.3, 16.2

group

control

—

—

—

treatment

-0.208

0.326

-0.846, 0.430

0.524

time_point

1st

—

—

—

2nd

-0.380

0.249

-0.869, 0.108

0.131

group * time_point

treatment * 2nd

0.864

0.379

0.120, 1.61

0.025

Pseudo R square

0.016

raq_difficulty

(Intercept)

12.5

0.178

12.2, 12.9

group

control

—

—

—

treatment

-0.234

0.250

-0.725, 0.256

0.350

time_point

1st

—

—

—

2nd

-0.153

0.183

-0.512, 0.205

0.403

group * time_point

treatment * 2nd

0.333

0.279

-0.213, 0.879

0.235

Pseudo R square

0.005

raq

(Intercept)

28.3

0.378

27.5, 29.0

group

control

—

—

—

treatment

-0.443

0.530

-1.48, 0.596

0.405

time_point

1st

—

—

—

2nd

-0.491

0.369

-1.21, 0.232

0.187

group * time_point

treatment * 2nd

1.16

0.563

0.052, 2.26

0.043

Pseudo R square

0.010

who

(Intercept)

14.6

0.574

13.5, 15.7

group

control

—

—

—

treatment

0.179

0.805

-1.40, 1.76

0.824

time_point

1st

—

—

—

2nd

-0.107

0.494

-1.07, 0.860

0.829

group * time_point

treatment * 2nd

0.718

0.757

-0.766, 2.20

0.346

Pseudo R square

0.005

phq

(Intercept)

3.66

0.484

2.71, 4.60

group

control

—

—

—

treatment

0.095

0.679

-1.24, 1.43

0.889

time_point

1st

—

—

—

2nd

0.121

0.340

-0.546, 0.788

0.722

group * time_point

treatment * 2nd

-0.129

0.524

-1.16, 0.898

0.806

Pseudo R square

0.000

gad

(Intercept)

3.38

0.456

2.49, 4.27

group

control

—

—

—

treatment

-0.329

0.639

-1.58, 0.924

0.607

time_point

1st

—

—

—

2nd

-0.181

0.376

-0.919, 0.556

0.631

group * time_point

treatment * 2nd

0.196

0.578

-0.937, 1.33

0.736

Pseudo R square

0.002

nb_pcs

(Intercept)

51.9

0.912

50.1, 53.7

group

control

—

—

—

treatment

-0.497

1.279

-3.00, 2.01

0.698

time_point

1st

—

—

—

2nd

-0.798

0.760

-2.29, 0.692

0.297

group * time_point

treatment * 2nd

1.51

1.167

-0.777, 3.80

0.199

Pseudo R square

0.003

nb_mcs

(Intercept)

50.2

1.103

48.0, 52.4

group

control

—

—

—

treatment

0.369

1.547

-2.66, 3.40

0.812

time_point

1st

—

—

—

2nd

1.12

1.080

-0.997, 3.24

0.303

group * time_point

treatment * 2nd

-0.728

1.650

-3.96, 2.51

0.660

Pseudo R square

0.003

1SE = Standard Error, CI = Confidence Interval

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.43) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.02 (95% CI [18.48, 19.56], t(194) = 68.87, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.41, 1.11], t(194) = 0.90, p = 0.367; Std. beta = 0.17, 95% CI [-0.19, 0.52])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.20, 95% CI [-0.83, 0.42], t(194) = -0.63, p = 0.529; Std. beta = -0.10, 95% CI [-0.39, 0.20])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.37, 95% CI [-0.57, 1.32], t(194) = 0.77, p = 0.440; Std. beta = 0.18, 95% CI [-0.27, 0.63])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 8.36e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.03 (95% CI [10.62, 11.45], t(194) = 51.66, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.27, 95% CI [-0.32, 0.85], t(194) = 0.89, p = 0.375; Std. beta = 0.16, 95% CI [-0.20, 0.52])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.30, 95% CI [-0.14, 0.74], t(194) = 1.34, p = 0.182; Std. beta = 0.18, 95% CI [-0.09, 0.45])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.83, 0.51], t(194) = -0.46, p = 0.647; Std. beta = -0.10, 95% CI [-0.50, 0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.69) and the part related to the fixed effects alone (marginal R2) is of 7.30e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.67 (95% CI [43.72, 45.62], t(194) = 92.10, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.46, 95% CI [-0.87, 1.79], t(194) = 0.68, p = 0.498; Std. beta = 0.12, 95% CI [-0.24, 0.49])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.25, 95% CI [-1.07, 0.57], t(194) = -0.61, p = 0.543; Std. beta = -0.07, 95% CI [-0.29, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.33, 95% CI [-0.93, 1.59], t(194) = 0.51, p = 0.607; Std. beta = 0.09, 95% CI [-0.25, 0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 9.02e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.41 (95% CI [14.81, 16.02], t(194) = 49.84, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.88, 0.82], t(194) = -0.07, p = 0.944; Std. beta = -0.01, 95% CI [-0.38, 0.35])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.13, 95% CI [-0.38, 0.63], t(194) = 0.50, p = 0.618; Std. beta = 0.06, 95% CI [-0.16, 0.27])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.50, 95% CI [-0.27, 1.28], t(194) = 1.27, p = 0.203; Std. beta = 0.22, 95% CI [-0.12, 0.55])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.14 (95% CI [11.77, 12.51], t(194) = 64.36, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.15, 95% CI [-0.37, 0.66], t(194) = 0.55, p = 0.583; Std. beta = 0.10, 95% CI [-0.26, 0.46])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.30, 95% CI [-0.66, 0.06], t(194) = -1.64, p = 0.101; Std. beta = -0.21, 95% CI [-0.46, 0.04])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.43, 95% CI [-0.12, 0.98], t(194) = 1.53, p = 0.126; Std. beta = 0.30, 95% CI [-0.08, 0.68])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.37) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.02 (95% CI [10.64, 11.39], t(194) = 57.40, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.20, 95% CI [-0.33, 0.73], t(194) = 0.74, p = 0.459; Std. beta = 0.14, 95% CI [-0.22, 0.50])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.36, 0.55], t(194) = 0.41, p = 0.679; Std. beta = 0.07, 95% CI [-0.25, 0.38])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.18, 95% CI [-0.51, 0.86], t(194) = 0.50, p = 0.616; Std. beta = 0.12, 95% CI [-0.35, 0.59])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.49) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.47 (95% CI [43.33, 45.60], t(194) = 76.54, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.50, 95% CI [-1.10, 2.10], t(194) = 0.62, p = 0.538; Std. beta = 0.11, 95% CI [-0.25, 0.47])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.95, 95% CI [-2.19, 0.29], t(194) = -1.50, p = 0.133; Std. beta = -0.21, 95% CI [-0.49, 0.07])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.36, 95% CI [-1.53, 2.24], t(194) = 0.37, p = 0.710; Std. beta = 0.08, 95% CI [-0.34, 0.51])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 6.84e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.67 (95% CI [24.63, 28.72], t(194) = 25.56, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.41, 95% CI [-3.27, 2.46], t(194) = -0.28, p = 0.782; Std. beta = -0.05, 95% CI [-0.42, 0.32])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.94, 95% CI [-0.94, 2.81], t(194) = 0.98, p = 0.328; Std. beta = 0.12, 95% CI [-0.12, 0.36])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.55, 95% CI [-4.43, 1.32], t(194) = -1.06, p = 0.289; Std. beta = -0.20, 95% CI [-0.57, 0.17])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.65) and the part related to the fixed effects alone (marginal R2) is of 9.53e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 45.21 (95% CI [42.29, 48.12], t(194) = 30.39, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.91, 95% CI [-5.00, 3.18], t(194) = -0.43, p = 0.664; Std. beta = -0.08, 95% CI [-0.44, 0.28])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 1.90, 95% CI [-0.75, 4.56], t(194) = 1.40, p = 0.160; Std. beta = 0.17, 95% CI [-0.07, 0.41])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.82, 95% CI [-5.89, 2.25], t(194) = -0.88, p = 0.381; Std. beta = -0.16, 95% CI [-0.52, 0.20])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 3.40e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.95 (95% CI [19.89, 22.00], t(194) = 38.97, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.17, 95% CI [-1.31, 1.65], t(194) = 0.22, p = 0.823; Std. beta = 0.04, 95% CI [-0.33, 0.41])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.13, 95% CI [-0.89, 1.15], t(194) = 0.26, p = 0.797; Std. beta = 0.03, 95% CI [-0.22, 0.29])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.11, 95% CI [-1.67, 1.44], t(194) = -0.14, p = 0.885; Std. beta = -0.03, 95% CI [-0.42, 0.36])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 9.82e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.12 (95% CI [12.39, 13.85], t(194) = 35.39, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.74, 1.30], t(194) = 0.54, p = 0.591; Std. beta = 0.10, 95% CI [-0.26, 0.46])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.24, 95% CI [-0.96, 0.49], t(194) = -0.64, p = 0.522; Std. beta = -0.08, 95% CI [-0.34, 0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.54, 95% CI [-0.56, 1.64], t(194) = 0.96, p = 0.337; Std. beta = 0.19, 95% CI [-0.20, 0.59])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 2.73e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.47 (95% CI [19.90, 21.03], t(194) = 71.17, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.17, 95% CI [-0.62, 0.96], t(194) = 0.42, p = 0.677; Std. beta = 0.08, 95% CI [-0.29, 0.44])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.08, 95% CI [-0.49, 0.65], t(194) = 0.29, p = 0.775; Std. beta = 0.04, 95% CI [-0.22, 0.30])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-0.78, 0.95], t(194) = 0.19, p = 0.848; Std. beta = 0.04, 95% CI [-0.36, 0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.41) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.60 (95% CI [4.34, 4.87], t(194) = 34.19, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.30, 95% CI [-0.07, 0.67], t(194) = 1.57, p = 0.116; Std. beta = 0.29, 95% CI [-0.07, 0.65])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.19, 0.43], t(194) = 0.76, p = 0.448; Std. beta = 0.12, 95% CI [-0.19, 0.42])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.38, 0.57], t(194) = 0.40, p = 0.687; Std. beta = 0.09, 95% CI [-0.36, 0.55])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.14 (95% CI [57.59, 60.69], t(194) = 74.75, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.91, 95% CI [-1.26, 3.09], t(194) = 0.82, p = 0.411; Std. beta = 0.15, 95% CI [-0.21, 0.52])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.11, 95% CI [-1.39, 1.62], t(194) = 0.15, p = 0.884; Std. beta = 0.02, 95% CI [-0.24, 0.27])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.62, 95% CI [-1.68, 2.92], t(194) = 0.53, p = 0.597; Std. beta = 0.11, 95% CI [-0.28, 0.50])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.51) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.74 (95% CI [15.29, 16.20], t(194) = 67.77, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.21, 95% CI [-0.85, 0.43], t(194) = -0.64, p = 0.523; Std. beta = -0.12, 95% CI [-0.48, 0.24])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.38, 95% CI [-0.87, 0.11], t(194) = -1.53, p = 0.127; Std. beta = -0.21, 95% CI [-0.49, 0.06])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 0.86, 95% CI [0.12, 1.61], t(194) = 2.28, p = 0.023; Std. beta = 0.49, 95% CI [0.07, 0.91])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.55) and the part related to the fixed effects alone (marginal R2) is of 4.92e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.53 (95% CI [12.18, 12.88], t(194) = 70.28, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.23, 95% CI [-0.72, 0.26], t(194) = -0.94, p = 0.349; Std. beta = -0.17, 95% CI [-0.54, 0.19])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.15, 95% CI [-0.51, 0.20], t(194) = -0.84, p = 0.401; Std. beta = -0.11, 95% CI [-0.38, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.33, 95% CI [-0.21, 0.88], t(194) = 1.19, p = 0.232; Std. beta = 0.25, 95% CI [-0.16, 0.65])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 9.60e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.28 (95% CI [27.54, 29.02], t(194) = 74.87, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.44, 95% CI [-1.48, 0.60], t(194) = -0.84, p = 0.403; Std. beta = -0.15, 95% CI [-0.52, 0.21])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.49, 95% CI [-1.21, 0.23], t(194) = -1.33, p = 0.183; Std. beta = -0.17, 95% CI [-0.42, 0.08])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 1.16, 95% CI [0.05, 2.26], t(194) = 2.05, p = 0.040; Std. beta = 0.40, 95% CI [0.02, 0.79])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.69) and the part related to the fixed effects alone (marginal R2) is of 4.83e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.62 (95% CI [13.50, 15.75], t(194) = 25.48, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.18, 95% CI [-1.40, 1.76], t(194) = 0.22, p = 0.824; Std. beta = 0.04, 95% CI [-0.32, 0.40])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.11, 95% CI [-1.07, 0.86], t(194) = -0.22, p = 0.828; Std. beta = -0.02, 95% CI [-0.25, 0.20])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.72, 95% CI [-0.77, 2.20], t(194) = 0.95, p = 0.343; Std. beta = 0.17, 95% CI [-0.18, 0.51])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.79) and the part related to the fixed effects alone (marginal R2) is of 1.69e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.66 (95% CI [2.71, 4.60], t(194) = 7.55, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-1.24, 1.43], t(194) = 0.14, p = 0.889; Std. beta = 0.03, 95% CI [-0.34, 0.39])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.55, 0.79], t(194) = 0.36, p = 0.722; Std. beta = 0.03, 95% CI [-0.15, 0.21])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-1.16, 0.90], t(194) = -0.25, p = 0.805; Std. beta = -0.04, 95% CI [-0.31, 0.24])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 1.58e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.38 (95% CI [2.49, 4.27], t(194) = 7.41, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.33, 95% CI [-1.58, 0.92], t(194) = -0.52, p = 0.607; Std. beta = -0.09, 95% CI [-0.45, 0.26])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.18, 95% CI [-0.92, 0.56], t(194) = -0.48, p = 0.630; Std. beta = -0.05, 95% CI [-0.26, 0.16])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.20, 95% CI [-0.94, 1.33], t(194) = 0.34, p = 0.735; Std. beta = 0.06, 95% CI [-0.27, 0.38])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 2.96e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.88 (95% CI [50.09, 53.67], t(194) = 56.89, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.50, 95% CI [-3.00, 2.01], t(194) = -0.39, p = 0.697; Std. beta = -0.07, 95% CI [-0.43, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.80, 95% CI [-2.29, 0.69], t(194) = -1.05, p = 0.294; Std. beta = -0.11, 95% CI [-0.33, 0.10])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 1.51, 95% CI [-0.78, 3.80], t(194) = 1.29, p = 0.196; Std. beta = 0.22, 95% CI [-0.11, 0.54])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 2.58e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.20 (95% CI [48.04, 52.37], t(194) = 45.51, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.37, 95% CI [-2.66, 3.40], t(194) = 0.24, p = 0.811; Std. beta = 0.04, 95% CI [-0.32, 0.41])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 1.12, 95% CI [-1.00, 3.24], t(194) = 1.04, p = 0.300; Std. beta = 0.13, 95% CI [-0.12, 0.39])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.73, 95% CI [-3.96, 2.51], t(194) = -0.44, p = 0.659; Std. beta = -0.09, 95% CI [-0.48, 0.30])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

sets

null

3

853.899

863.793

-423.949

847.899

sets

random

6

857.193

876.983

-422.596

845.193

2.706

3

0.439

setv

null

3

740.303

750.198

-367.152

734.303

setv

random

6

743.745

763.535

-365.873

731.745

2.558

3

0.465

maks

null

3

1,041.871

1,051.765

-517.935

1,035.871

maks

random

6

1,046.603

1,066.393

-517.302

1,034.603

1.267

3

0.737

ibs

null

3

860.393

870.288

-427.197

854.393

ibs

random

6

861.635

881.425

-424.818

849.635

4.758

3

0.190

ers_e

null

3

683.624

693.519

-338.812

677.624

ers_e

random

6

684.868

704.657

-336.434

672.868

4.756

3

0.191

ers_r

null

3

712.472

722.367

-353.236

706.472

ers_r

random

6

716.097

735.887

-352.048

704.097

2.375

3

0.498

pss_pa

null

3

1,146.225

1,156.120

-570.113

1,140.225

pss_pa

random

6

1,148.310

1,168.100

-568.155

1,136.310

3.916

3

0.271

pss_ps

null

3

1,357.294

1,367.189

-675.647

1,351.294

pss_ps

random

6

1,361.470

1,381.260

-674.735

1,349.470

1.824

3

0.610

pss

null

3

1,499.020

1,508.915

-746.510

1,493.020

pss

random

6

1,502.255

1,522.045

-745.127

1,490.255

2.765

3

0.429

rki_responsible

null

3

1,097.209

1,107.104

-545.604

1,091.209

rki_responsible

random

6

1,103.112

1,122.902

-545.556

1,091.112

0.097

3

0.992

rki_nonlinear

null

3

953.657

963.552

-473.828

947.657

rki_nonlinear

random

6

957.713

977.503

-472.857

945.713

1.944

3

0.584

rki_peer

null

3

852.516

862.411

-423.258

846.516

rki_peer

random

6

857.921

877.711

-422.960

845.921

0.595

3

0.898

rki_expect

null

3

571.795

581.690

-282.898

565.795

rki_expect

random

6

572.105

591.895

-280.053

560.105

5.690

3

0.128

rki

null

3

1,253.897

1,263.792

-623.949

1,247.897

rki

random

6

1,258.028

1,277.818

-623.014

1,246.028

1.870

3

0.600

raq_possible

null

3

779.206

789.101

-386.603

773.206

raq_possible

random

6

779.845

799.635

-383.923

767.845

5.361

3

0.147

raq_difficulty

null

3

664.506

674.401

-329.253

658.506

raq_difficulty

random

6

668.806

688.596

-328.403

656.806

1.700

3

0.637

raq

null

3

961.130

971.025

-477.565

955.130

raq

random

6

962.879

982.669

-475.440

950.879

4.251

3

0.236

who

null

3

1,108.901

1,118.796

-551.450

1,102.901

who

random

6

1,113.402

1,133.192

-550.701

1,101.402

1.499

3

0.682

phq

null

3

1,010.756

1,020.651

-502.378

1,004.756

phq

random

6

1,016.622

1,036.412

-502.311

1,004.622

0.134

3

0.987

gad

null

3

1,010.151

1,020.046

-502.076

1,004.151

gad

random

6

1,015.741

1,035.531

-501.871

1,003.741

0.410

3

0.938

nb_pcs

null

3

1,290.198

1,300.093

-642.099

1,284.198

nb_pcs

random

6

1,294.425

1,314.215

-641.213

1,282.425

1.773

3

0.621

nb_mcs

null

3

1,387.195

1,397.090

-690.598

1,381.195

nb_mcs

random

6

1,392.001

1,411.791

-690.001

1,380.001

1.194

3

0.754

Post hoc analysis text

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

sets

1st

58

19.02 ± 2.10

60

19.37 ± 2.10

0.368

-0.218

sets

2nd

48

18.82 ± 2.07

0.125

34

19.54 ± 2.03

-0.108

0.117

-0.451

setv

1st

58

11.03 ± 1.63

60

11.30 ± 1.63

0.377

-0.237

setv

2nd

48

11.33 ± 1.59

-0.267

34

11.44 ± 1.53

-0.127

0.755

-0.097

maks

1st

58

44.67 ± 3.69

60

45.13 ± 3.69

0.499

-0.222

maks

2nd

48

44.42 ± 3.54

0.122

34

45.21 ± 3.30

-0.036

0.302

-0.380

ibs

1st

58

15.41 ± 2.36

60

15.38 ± 2.36

0.944

0.024

ibs

2nd

48

15.54 ± 2.25

-0.100

34

16.01 ± 2.09

-0.494

0.330

-0.370

ers_e

1st

58

12.14 ± 1.44

60

12.28 ± 1.44

0.583

-0.159

ers_e

2nd

48

11.84 ± 1.39

0.329

34

12.41 ± 1.33

-0.139

0.060

-0.627

ers_r

1st

58

11.02 ± 1.46

60

11.22 ± 1.46

0.460

-0.170

ers_r

2nd

48

11.11 ± 1.45

-0.082

34

11.49 ± 1.43

-0.233

0.245

-0.321

pss_pa

1st

58

44.47 ± 4.42

60

44.97 ± 4.42

0.539

-0.158

pss_pa

2nd

48

43.52 ± 4.34

0.299

34

44.37 ± 4.21

0.187

0.370

-0.271

pss_ps

1st

58

26.67 ± 7.95

60

26.27 ± 7.95

0.782

0.085

pss_ps

2nd

48

27.61 ± 7.67

-0.196

34

25.65 ± 7.22

0.130

0.240

0.411

pss

1st

58

45.21 ± 11.33

60

44.30 ± 11.33

0.664

0.134

pss

2nd

48

47.11 ± 10.92

-0.282

34

44.39 ± 10.28

-0.013

0.251

0.404

rki_responsible

1st

58

20.95 ± 4.09

60

21.12 ± 4.09

0.824

-0.065

rki_responsible

2nd

48

21.08 ± 3.97

-0.052

34

21.14 ± 3.77

-0.007

0.951

-0.021

rki_nonlinear

1st

58

13.12 ± 2.82

60

13.40 ± 2.82

0.592

-0.152

rki_nonlinear

2nd

48

12.88 ± 2.74

0.128

34

13.70 ± 2.62

-0.165

0.173

-0.445

rki_peer

1st

58

20.47 ± 2.19

60

20.63 ± 2.19

0.678

-0.116

rki_peer

2nd

48

20.55 ± 2.13

-0.057

34

20.80 ± 2.04

-0.116

0.588

-0.175

rki_expect

1st

58

4.60 ± 1.03

60

4.90 ± 1.03

0.118

-0.370

rki_expect

2nd

48

4.72 ± 1.01

-0.151

34

5.12 ± 1.00

-0.272

0.081

-0.491

rki

1st

58

59.14 ± 6.03

60

60.05 ± 6.03

0.412

-0.238

rki

2nd

48

59.25 ± 5.84

-0.029

34

60.78 ± 5.56

-0.191

0.230

-0.400

raq_possible

1st

58

15.74 ± 1.77

60

15.53 ± 1.77

0.524

0.167

raq_possible

2nd

48

15.36 ± 1.73

0.305

34

16.02 ± 1.67

-0.387

0.087

-0.525

raq_difficulty

1st

58

12.53 ± 1.36

60

12.30 ± 1.36

0.350

0.257

raq_difficulty

2nd

48

12.38 ± 1.32

0.168

34

12.48 ± 1.27

-0.196

0.735

-0.108

raq

1st

58

28.28 ± 2.88

60

27.83 ± 2.88

0.405

0.241

raq

2nd

48

27.79 ± 2.79

0.267

34

28.50 ± 2.66

-0.362

0.242

-0.388

who

1st

58

14.62 ± 4.37

60

14.80 ± 4.37

0.824

-0.073

who

2nd

48

14.51 ± 4.19

0.044

34

15.41 ± 3.91

-0.249

0.322

-0.366

phq

1st

58

3.66 ± 3.69

60

3.75 ± 3.69

0.889

-0.056

phq

2nd

48

3.78 ± 3.48

-0.072

34

3.74 ± 3.15

0.005

0.963

0.020

gad

1st

58

3.38 ± 3.47

60

3.05 ± 3.47

0.607

0.176

gad

2nd

48

3.20 ± 3.32

0.097

34

3.06 ± 3.07

-0.008

0.851

0.072

nb_pcs

1st

58

51.88 ± 6.94

60

51.38 ± 6.94

0.698

0.132

nb_pcs

2nd

48

51.08 ± 6.64

0.212

34

52.09 ± 6.16

-0.189

0.479

-0.268

nb_mcs

1st

58

50.20 ± 8.40

60

50.57 ± 8.40

0.812

-0.069

nb_mcs

2nd

48

51.32 ± 8.15

-0.208

34

50.97 ± 7.76

-0.073

0.840

0.066

Between group

sets

1st

t(172.28) = 0.90, p = 0.368, Cohen d = -0.22, 95% CI (-0.41 to 1.11)

2st

t(193.56) = 1.58, p = 0.117, Cohen d = -0.45, 95% CI (-0.18 to 1.63)

setv

1st

t(161.44) = 0.89, p = 0.377, Cohen d = -0.24, 95% CI (-0.33 to 0.86)

2st

t(190.53) = 0.31, p = 0.755, Cohen d = -0.10, 95% CI (-0.58 to 0.80)

maks

1st

t(145.06) = 0.68, p = 0.499, Cohen d = -0.22, 95% CI (-0.88 to 1.81)

2st

t(180.21) = 1.04, p = 0.302, Cohen d = -0.38, 95% CI (-0.72 to 2.30)

ibs

1st

t(142.79) = -0.07, p = 0.944, Cohen d = 0.02, 95% CI (-0.89 to 0.83)

2st

t(177.87) = 0.98, p = 0.330, Cohen d = -0.37, 95% CI (-0.48 to 1.43)

ers_e

1st

t(154.38) = 0.55, p = 0.583, Cohen d = -0.16, 95% CI (-0.38 to 0.67)

2st

t(187.23) = 1.89, p = 0.060, Cohen d = -0.63, 95% CI (-0.02 to 1.17)

ers_r

1st

t(177.78) = 0.74, p = 0.460, Cohen d = -0.17, 95% CI (-0.33 to 0.73)

2st

t(194.49) = 1.17, p = 0.245, Cohen d = -0.32, 95% CI (-0.26 to 1.01)

pss_pa

1st

t(165.45) = 0.62, p = 0.539, Cohen d = -0.16, 95% CI (-1.11 to 2.11)

2st

t(191.88) = 0.90, p = 0.370, Cohen d = -0.27, 95% CI (-1.03 to 2.74)

pss_ps

1st

t(149.48) = -0.28, p = 0.782, Cohen d = 0.09, 95% CI (-3.30 to 2.49)

2st

t(184.00) = -1.18, p = 0.240, Cohen d = 0.41, 95% CI (-5.24 to 1.32)

pss

1st

t(148.95) = -0.43, p = 0.664, Cohen d = 0.13, 95% CI (-5.03 to 3.21)

2st

t(183.60) = -1.15, p = 0.251, Cohen d = 0.40, 95% CI (-7.39 to 1.94)

rki_responsible

1st

t(153.88) = 0.22, p = 0.824, Cohen d = -0.06, 95% CI (-1.32 to 1.66)

2st

t(186.94) = 0.06, p = 0.951, Cohen d = -0.02, 95% CI (-1.65 to 1.76)

rki_nonlinear

1st

t(156.21) = 0.54, p = 0.592, Cohen d = -0.15, 95% CI (-0.75 to 1.31)

2st

t(188.22) = 1.37, p = 0.173, Cohen d = -0.45, 95% CI (-0.36 to 2.00)

rki_peer

1st

t(157.54) = 0.42, p = 0.678, Cohen d = -0.12, 95% CI (-0.63 to 0.96)

2st

t(188.88) = 0.54, p = 0.588, Cohen d = -0.17, 95% CI (-0.67 to 1.17)

rki_expect

1st

t(175.02) = 1.57, p = 0.118, Cohen d = -0.37, 95% CI (-0.08 to 0.67)

2st

t(194.06) = 1.75, p = 0.081, Cohen d = -0.49, 95% CI (-0.05 to 0.84)

rki

1st

t(154.11) = 0.82, p = 0.412, Cohen d = -0.24, 95% CI (-1.28 to 3.10)

2st

t(187.08) = 1.20, p = 0.230, Cohen d = -0.40, 95% CI (-0.98 to 4.04)

raq_possible

1st

t(163.78) = -0.64, p = 0.524, Cohen d = 0.17, 95% CI (-0.85 to 0.44)

2st

t(191.36) = 1.72, p = 0.087, Cohen d = -0.53, 95% CI (-0.10 to 1.41)

raq_difficulty

1st

t(159.08) = -0.94, p = 0.350, Cohen d = 0.26, 95% CI (-0.73 to 0.26)

2st

t(189.58) = 0.34, p = 0.735, Cohen d = -0.11, 95% CI (-0.47 to 0.67)

raq

1st

t(154.58) = -0.84, p = 0.405, Cohen d = 0.24, 95% CI (-1.49 to 0.60)

2st

t(187.34) = 1.17, p = 0.242, Cohen d = -0.39, 95% CI (-0.49 to 1.91)

who

1st

t(144.88) = 0.22, p = 0.824, Cohen d = -0.07, 95% CI (-1.41 to 1.77)

2st

t(180.04) = 0.99, p = 0.322, Cohen d = -0.37, 95% CI (-0.88 to 2.68)

phq

1st

t(134.35) = 0.14, p = 0.889, Cohen d = -0.06, 95% CI (-1.25 to 1.44)

2st

t(166.08) = -0.05, p = 0.963, Cohen d = 0.02, 95% CI (-1.49 to 1.42)

gad

1st

t(142.28) = -0.52, p = 0.607, Cohen d = 0.18, 95% CI (-1.59 to 0.93)

2st

t(177.30) = -0.19, p = 0.851, Cohen d = 0.07, 95% CI (-1.54 to 1.27)

nb_pcs

1st

t(142.90) = -0.39, p = 0.698, Cohen d = 0.13, 95% CI (-3.03 to 2.03)

2st

t(177.98) = 0.71, p = 0.479, Cohen d = -0.27, 95% CI (-1.80 to 3.83)

nb_mcs

1st

t(154.83) = 0.24, p = 0.812, Cohen d = -0.07, 95% CI (-2.69 to 3.43)

2st

t(187.48) = -0.20, p = 0.840, Cohen d = 0.07, 95% CI (-3.86 to 3.15)

Within treatment group

sets

1st vs 2st

t(103.04) = 0.47, p = 0.636, Cohen d = -0.11, 95% CI (-0.55 to 0.90)

setv

1st vs 2st

t(98.49) = 0.55, p = 0.582, Cohen d = -0.13, 95% CI (-0.37 to 0.66)

maks

1st vs 2st

t(91.94) = 0.15, p = 0.877, Cohen d = -0.04, 95% CI (-0.89 to 1.04)

ibs

1st vs 2st

t(91.04) = 2.10, p = 0.038, Cohen d = -0.49, 95% CI (0.03 to 1.23)

ers_e

1st vs 2st

t(95.65) = 0.60, p = 0.551, Cohen d = -0.14, 95% CI (-0.30 to 0.55)

ers_r

1st vs 2st

t(105.51) = 1.03, p = 0.306, Cohen d = -0.23, 95% CI (-0.25 to 0.80)

pss_pa

1st vs 2st

t(100.14) = -0.81, p = 0.418, Cohen d = 0.19, 95% CI (-2.04 to 0.85)

pss_ps

1st vs 2st

t(93.70) = -0.55, p = 0.580, Cohen d = 0.13, 95% CI (-2.83 to 1.59)

pss

1st vs 2st

t(93.49) = 0.05, p = 0.957, Cohen d = -0.01, 95% CI (-3.04 to 3.21)

rki_responsible

1st vs 2st

t(95.45) = 0.03, p = 0.975, Cohen d = -0.01, 95% CI (-1.18 to 1.22)

rki_nonlinear

1st vs 2st

t(96.38) = 0.71, p = 0.478, Cohen d = -0.16, 95% CI (-0.54 to 1.15)

rki_peer

1st vs 2st

t(96.92) = 0.50, p = 0.618, Cohen d = -0.12, 95% CI (-0.50 to 0.83)

rki_expect

1st vs 2st

t(104.25) = 1.20, p = 0.233, Cohen d = -0.27, 95% CI (-0.14 to 0.58)

rki

1st vs 2st

t(95.55) = 0.82, p = 0.412, Cohen d = -0.19, 95% CI (-1.03 to 2.50)

raq_possible

1st vs 2st

t(99.45) = 1.68, p = 0.095, Cohen d = -0.39, 95% CI (-0.09 to 1.05)

raq_difficulty

1st vs 2st

t(97.54) = 0.85, p = 0.397, Cohen d = -0.20, 95% CI (-0.24 to 0.60)

raq

1st vs 2st

t(95.73) = 1.56, p = 0.122, Cohen d = -0.36, 95% CI (-0.18 to 1.51)

who

1st vs 2st

t(91.87) = 1.06, p = 0.291, Cohen d = -0.25, 95% CI (-0.53 to 1.75)

phq

1st vs 2st

t(87.66) = -0.02, p = 0.984, Cohen d = 0.00, 95% CI (-0.80 to 0.79)

gad

1st vs 2st

t(90.84) = 0.03, p = 0.974, Cohen d = -0.01, 95% CI (-0.86 to 0.89)

nb_pcs

1st vs 2st

t(91.08) = 0.80, p = 0.424, Cohen d = -0.19, 95% CI (-1.05 to 2.47)

nb_mcs

1st vs 2st

t(95.83) = 0.31, p = 0.754, Cohen d = -0.07, 95% CI (-2.09 to 2.88)

Within control group

sets

1st vs 2st

t(89.46) = -0.63, p = 0.531, Cohen d = 0.13, 95% CI (-0.84 to 0.43)

setv

1st vs 2st

t(87.50) = 1.33, p = 0.186, Cohen d = -0.27, 95% CI (-0.15 to 0.75)

maks

1st vs 2st

t(84.78) = -0.61, p = 0.545, Cohen d = 0.12, 95% CI (-1.09 to 0.58)

ibs

1st vs 2st

t(84.42) = 0.50, p = 0.620, Cohen d = -0.10, 95% CI (-0.38 to 0.64)

ers_e

1st vs 2st

t(86.31) = -1.64, p = 0.105, Cohen d = 0.33, 95% CI (-0.67 to 0.06)

ers_r

1st vs 2st

t(90.56) = 0.41, p = 0.680, Cohen d = -0.08, 95% CI (-0.37 to 0.56)

pss_pa

1st vs 2st

t(88.20) = -1.50, p = 0.137, Cohen d = 0.30, 95% CI (-2.21 to 0.31)

pss_ps

1st vs 2st

t(85.50) = 0.98, p = 0.332, Cohen d = -0.20, 95% CI (-0.97 to 2.84)

pss

1st vs 2st

t(85.41) = 1.40, p = 0.164, Cohen d = -0.28, 95% CI (-0.79 to 4.60)

rki_responsible

1st vs 2st

t(86.22) = 0.26, p = 0.798, Cohen d = -0.05, 95% CI (-0.90 to 1.17)

rki_nonlinear

1st vs 2st

t(86.61) = -0.64, p = 0.524, Cohen d = 0.13, 95% CI (-0.97 to 0.50)

rki_peer

1st vs 2st

t(86.83) = 0.29, p = 0.776, Cohen d = -0.06, 95% CI (-0.49 to 0.66)

rki_expect

1st vs 2st

t(90.00) = 0.76, p = 0.451, Cohen d = -0.15, 95% CI (-0.20 to 0.44)

rki

1st vs 2st

t(86.26) = 0.15, p = 0.884, Cohen d = -0.03, 95% CI (-1.42 to 1.64)

raq_possible

1st vs 2st

t(87.90) = -1.52, p = 0.131, Cohen d = 0.30, 95% CI (-0.88 to 0.12)

raq_difficulty

1st vs 2st

t(87.09) = -0.84, p = 0.404, Cohen d = 0.17, 95% CI (-0.52 to 0.21)

raq

1st vs 2st

t(86.34) = -1.33, p = 0.187, Cohen d = 0.27, 95% CI (-1.22 to 0.24)

who

1st vs 2st

t(84.75) = -0.22, p = 0.829, Cohen d = 0.04, 95% CI (-1.09 to 0.88)

phq

1st vs 2st

t(83.05) = 0.36, p = 0.723, Cohen d = -0.07, 95% CI (-0.56 to 0.80)

gad

1st vs 2st

t(84.33) = -0.48, p = 0.632, Cohen d = 0.10, 95% CI (-0.93 to 0.57)

nb_pcs

1st vs 2st

t(84.43) = -1.05, p = 0.297, Cohen d = 0.21, 95% CI (-2.31 to 0.71)

nb_mcs

1st vs 2st

t(86.38) = 1.04, p = 0.303, Cohen d = -0.21, 95% CI (-1.03 to 3.27)

Plot